Generative Adversarial Networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some recent GANs (e.g., InfoGAN), are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images are with different properties due to the imaging mechanism. Despite the success of InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties, thus editing specific properties usually relies on empirical trials, unreliable and time-consuming. In this paper, we show that latent codes are disentangled to affect the properties of SAR images in a non-linear manner. By introducing some property estimators for latent codes, we are able to provide a completely analytical nonlinear model to decompose the entangled causality between latent codes and different properties. The qualitative and quantitative experimental results further reveal that the properties can be calculated by latent codes, inversely, the satisfying latent codes can be estimated given desired properties. In this case, properties can be manipulated by latent codes as we expect.
翻译:合成合成孔径雷达(SAR)图像。最近的一些GAN(例如InfoGAN)甚至能够通过引入潜在代码来编辑合成图像的具体属性。这对于合成图像合成至关重要,因为真实合成孔径雷达图像中的目标由于图像机制的不同属性而具有不同的属性。尽管InfoGAN在操纵属性方面取得成功,但这些潜在代码如何影响合成属性,仍然缺乏清晰的解释,因此编辑特定属性通常依赖于经验性试验、不可靠和耗时。在本文件中,我们表明潜在代码被分解,以非线性方式影响合成图像的属性。通过引入一些潜在代码的属性估计符,我们可以提供一个完全分析的非线性模型,将潜在代码与不同属性之间的因果关系分解。定性和定量实验结果进一步表明,这些属性可以通过潜在代码进行计算,反之,满足的潜在代码可以按预期的特性进行估计。在这种情况下,通过引入一些隐性代码,这些属性可以被我们所期望的潜在代码加以操纵。